摘要
该文提出一种基于杂散磁通密度信号立体螺旋曲线投影面变换与ResNeXt-18深度学习框架相结合的方法,以实现永磁同步直线电机(PMSLM)偏心故障的非侵入式诊断。首先,建立PMSLM有限元模型,分析静态和动态偏心故障下的电机内部与杂散磁场分布。采用隧道磁阻效应(TMR)传感器并设计连接件,实现传感器与电机动子一体化设计,对电机外部杂散磁通密度信号进行实时非接触式测量。其次,引入立体螺旋曲线变换(TDSCT)信号处理算法,对电机偏心故障下的外部杂散磁通密度一维信号进行三维调制,并通过对多视角下二维投影面图像的拼接融合,实现故障特征的可视化增强。然后,引用深度学习ResNeXt-18分类框架,通过对杂散磁通密度信号二维投影面数据集的训练学习,实现偏心故障的定量精细化诊断,精度高达99.4%。与Xception,ResNet-18,GoogLeNet和CNN的对比实验表明,ResNeXt-18具有更高的诊断精度和鲁棒性。最后,搭建PMSLM样机实验平台,验证了该文所提方法的有效性。
Permanent magnet synchronous linear motor(PMSLM)has the advantages of high transmission efficiency,high thrust quality and high positioning accuracy.It is widely used in industrial scenarios with linear direct drive such as high-precision machine tools,parallel robots,etc.In actual industrial applications,PMSLM may produce eccentricity faults due to complex operating conditions,mechanical assembly errors and other factors.The traditional eccentricity fault diagnosis method based on electrical signal is very easy to be affected by environmental noise,operating conditions and other factors,and it is still not enough to directly reflect the state of the motor eccentricity fault.In recent years,some fault diagnosis methods for motor eccentricity based on magnetic density signal which can directly reflect the state of the motor eccentricity fault have been proposed,but most of them exist some drawbacks,such as cumbersome sensor installation steps,insufficient sensor sensitivity and incomplete fault information feature extraction.To solve the above problems,this paper proposes eccentricity fault diagnosis of permanent magnet linear motor based on three-dimensional spiral curve transformation of stray magnetic field signal and ResNeXt-18.Firstly,finite element simulation models under healthy state and eccentricity fault state of PMSLM are established to obtain the external stray magnetic density signal of PMSLM.Secondly,the eccentricity fault feature enhancement signal processing method based on three-dimensional spiral curve transformation(TDSCT)is applied to transform the external stray magnetic density signal into two-dimensional eccentricity fault feature imagein order to realize fault feature enhancement display.Thirdly,the dataset is input into the deep learning ResNeXt-18 classification framework,through which various fault signal characteristics are obtained and accurate fault diagnosis is achieved.In simulation experiments,the validation accuracy of ResNeXt-18 improves 1.3%,1.3%,1.6%and 3.8%compared with Xcepti
作者
钱龙
吴先红
宋俊材
陆思良
王骁贤
Qian Long;Wu Xianhong;Song Juncai;Lu Siliang;Wang Xiaoxian(School of Electrical Engineering and Automation Anhui University,Hefei 230601 China;School of Internet Anhui University,Hefei 230039 China;School of Electronic and Information Engineering Anhui University,Hefei 230601 China)
出处
《电工技术学报》
EI
CSCD
北大核心
2024年第18期5705-5718,共14页
Transactions of China Electrotechnical Society
基金
国家自然科学基金面上项目(52075002)
国家自然科学基金青年项目(52207036)
安徽省自然科学基金青年项目(2208085QE167)
安徽省教育厅自然科学重点项目(KJ2021A0018)资助。
关键词
永磁同步直线电机
偏心故障诊断
外部杂散磁场信号
立体螺旋曲线变换
ResNeXt-18
Permanent magnet synchronous linear motor(PMSLM)
eccentricity fault diagnosis
externalstray magnetic field
three-dimensional spiral curve transformation(TDSCT)
ResNeXt-18
